Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

References

• Abbas, O., C. Rebufa, N. Dupuy, A. Permanyer, and J. Kister. 2012. PLS regression on spectroscopic data for the prediction of crude oil quality: API gravity and aliphatic/aromatic ratio. Fuel 98:5–14. doi: 10.1016/j.fuel.2012.03.045.
   Web of Science ®Google Scholar
• Andersson, M. 2009. A comparison of nine PLS1 algorithms. Journal of Chemometrics 23(10):518–29. doi: 10.1002/cem.1248.
   Web of Science ®Google Scholar
• ANP. 2019. Brazilian petroleum, natural gas, and biofuels agency. Ordinance n. 09/2000. Last Modified January 21, 2000. http://www.anp.gov.br (accessed January 20, 2019).
   Google Scholar
• Ardila, J. A., F. L. F. Soares, M. A. S. Farias, and R. L. Carneiro. 2017. Characterization of gasoline by Raman spectroscopy with chemometric analysis. Analytical Letters 50(7):1126–38. doi: 10.1080/00032719.2016.1210616.
   Web of Science ®Google Scholar
• ASTM D1250. 2008. Standard guide for use of the petroleum measurement tables. West Conshohocken, PA: ASTM International.
   Google Scholar
• ASTM D4629. 2012. Standard test method for trace nitrogen in liquid petroleum hydrocarbons by syringe/inlet oxidative combustion and chemiluminescence detection. West Conshohocken, PA: ASTM International.
   Google Scholar
• ASTM E1655. 2012. Standards practices for infrared, multivariate, quantitative analysis. West Conshohocken, PA: ASTM International.
   Google Scholar
• ASTM UOP 269. 2010. Nitrogen bases in hydrocarbons by titration. West Conshohocken, PA: ASTM International.
   Google Scholar
• Blanco, M., J. Coello, H. Iturriaga, S. Maspoch, and C. de la Pezuela. 1997. Effect of data preprocessing methods in near-infrared diffuse reflectance spectroscopy for the determination of the active compound in a pharmaceutical preparation. Applied Spectroscopy 51(2):240–6. doi: 10.1366/0003702971939947.
   Web of Science ®Google Scholar
• Borin, A., and R. J. Poppi. 2005. Application of mid infrared spectroscopy and iPLS for the quantification of contaminants in lubricating oil. Vibrational Spectroscopy 37(1):27–32. doi: 10.1016/j.vibspec.2004.05.003.
   Web of Science ®Google Scholar
• Brereton, R. G. 2000. Introduction to multivariate calibration in analytical chemistry. The Analyst 125(11):2125–54. doi: 10.1039/b003805i.
   Web of Science ®Google Scholar
• Brereton, R. G. 2003. Chemometrics: Data analysis for the laboratory and chemical plant. Chichester, UK: John Wiley & Sons.
   Google Scholar
• Bylesjö, M., D. Eriksson, A. Sjödin, S. Jansson, T. Moritz, and J. Trygg. 2007. Orthogonal projections to latent structures as a strategy for microarray data normalization. BMC Bioinformatics 8:207. doi: 10.1186/1471-2105-8-207.
   PubMed Web of Science ®Google Scholar
• Chen, H., Z. Lin, and C. Tan. 2018. Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models. Vibrational Spectroscopy 99:178–83. doi: 10.1016/j.vibspec.2018.10.003.
   Web of Science ®Google Scholar
• Chen, H., Z. Liu, K. Cai, L. Xu, and A. Chen. 2018a. Grid search parametric optimization for FT-NIR quantitative analysis of solid soluble content in strawberry samples. Vibrational Spectroscopy 97:7–15. doi: 10.1016/j.vibspec.2017.10.006.
   Web of Science ®Google Scholar
• Chen, H., Z. Liu, J. Gu, W. Ai, J. Wen, and K. Cai. 2018b. Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning. Analytical Methods 10(41):5004–13. doi: 10.1039/C8AY01076E.
   Web of Science ®Google Scholar
• Chung, H., and M. Ku. 2000. Comparison of near-infrared, infrared, and Raman spectroscopy for the analysis of heavy petroleum products. Applied Spectroscopy 54(2):239–45. doi: 10.1366/0003702001949168.
   Web of Science ®Google Scholar
• Chung, H. 2007. Applications of near‐infrared spectroscopy in refineries and important issues to address. Applied Spectroscopy Reviews 42(3):251–85. doi: 10.1080/05704920701293778.
   Web of Science ®Google Scholar
• Duarte, L. M., P. R. Filgueiras, S. R. C. Silva, J. C. M. Dias, L. M. S. L. Oliveira, E. V. R. Castro, and M. A. L. Oliveira. 2016. Determination of some physicochemical properties in Brazilian crude oil by 1H NMR spectroscopy associated to chemometric approach. Fuel 181:660–9. doi: 10.1016/j.fuel.2016.05.049.
   Web of Science ®Google Scholar
• El-Bassoussi, A. A., M. H. M. Ahmed, S. M. E. Sayed, J. S. Basta, and E. S. K. Attia. 2010. Characterization of some local petroleum residues by spectroscopic techniques. Petroleum Science and Technology 28(5):430–44. doi: 10.1080/10916460902744554.
   Web of Science ®Google Scholar
• Eriksson, L., J. Rosén, E. Johansson, and J. Trygg. 2012. Orthogonal PLS (OPLS) modeling for improved analysis and interpretation in drug design. Molecular Informatics 31(6–7):414–9. doi: 10.1002/minf.201200158.
   PubMed Web of Science ®Google Scholar
• Ferrão, M. F., M. S. Viera, R. E. P. Pazos, D. Fachini, A. E. Gerbase, and L. Marder. 2011. Simultaneous determination of quality parameters of biodiesel/diesel blends using HATR-FTIR spectra and PLS, iPLS or siPLS regressions. Fuel 90(2):701–6. doi: 10.1016/j.fuel.2010.09.016.
   Web of Science ®Google Scholar
• Filgueiras, P. R., C. M. S. Sad, A. R. Loureiro, M. F. P. Santos, E. V. R. Castro, J. C. M. Dias, and R. J. Poppi. 2014a. Determination of API gravity, kinematic viscosity and water content in petroleum by ATR-FTIR spectroscopy and multivariate calibration. Fuel 116:123–30. doi: 10.1016/j.fuel.2013.07.122.
   Web of Science ®Google Scholar
• Filgueiras, P. R., J. C. L. Alves, C. M. S. Sad, E. V. R. Castro, J. C. M. Dias, and R. J. Poppi. 2014b. Evaluation of trends in residuals of multivariate calibration models by permutation test. Chemometrics and Intelligent Laboratory Systems 133:33–41. doi: 10.1016/j.chemolab.2014.02.002.
   Web of Science ®Google Scholar
• Gabrielsson, J., H. Jonsson, C. Airiau, B. Schmidt, R. Escott, and J. Trygg. 2006. OPLS methodology for analysis of pre-processing effects on spectroscopic data. Chemometrics and Intelligent Laboratory Systems 84(1–2):153–8. doi: 10.1016/j.chemolab.2006.03.013.
   Web of Science ®Google Scholar
• Gan, F., G. Ruan, and J. Mo. 2006. Baseline correction by improved iterative polynomial fitting with automatic threshold. Chemometrics and Intelligent Laboratory Systems 82(1–2):59–65. doi: 10.1016/j.chemolab.2005.08.009.
   Web of Science ®Google Scholar
• Geladi, P., and B. R. Kowalski. 1986. Partial least-squares regression: A tutorial. Analytica Chimica Acta 185:1–17. doi: 10.1016/0003-2670(86)80028-9.
   Web of Science ®Google Scholar
• Höskuldsson, A. 1988. PLS regression methods. Journal of Chemometrics 2(3):211–28. doi: 10.1002/cem.1180020306.
   Google Scholar
• ISO 12185. 1996. Determination of density – Oscillating u-tube method. Geneva, Switzerland: International Organization for Standardization.
   Google Scholar
• Jingyan, L., C. Xiaoli, and T. Songbai. 2012. Research on determination of total acid number of petroleum using mid-infrared attenuated total reflection spectroscopy. Energy & Fuels 26(9):5633–7. doi: 10.1021/ef3002372.
   Web of Science ®Google Scholar
• Jong, S. 1993. SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 18:251–63. doi: 10.1016/0169-7439(93)85002-X.
   Web of Science ®Google Scholar
• Kennard, R. W., and L. A. Stone. 1969. Computer aided design of experiments. Technometrics 11(1):137–48. doi: 10.1080/00401706.1969.10490666.
   Web of Science ®Google Scholar
• Li, B. Y., Y. Z. Liang, Y. Hu, Y. Q. Du, Y. Q. Song, and H. Cui. 2003. Evaluation of gas chromatography–mass spectrometry in conjunction with chemometric resolution for identification of nitrogen compounds in crude oil. Talanta 61(6):803–9. doi: 10.1016/S0039-9140(03)00383-7.
   PubMed Web of Science ®Google Scholar
• Li, H., Y. Liang, Q. Xu, and D. Cao. 2009. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 648(1):77–84. doi: 10.1016/j.aca.2009.06.046.
   PubMed Web of Science ®Google Scholar
• Li, H., L. Liang, Y. Q. Xu, and D. Cao. 2010. Model population analysis for variable selection. Journal of Chemometrics 24(7–8):418–23. doi: 10.1002/cem.1300.
   Web of Science ®Google Scholar
• Liang, C., H. Yuan, Z. Zhao, C. Song, and J. Wang. 2016. A new multivariate calibration model transfer method of near-infrared spectral analysis. Chemometrics and Intelligent Laboratory Systems 153:51–7. doi: 10.1016/j.chemolab.2016.01.017.
   Web of Science ®Google Scholar
• Lorber, A. 1986. Error propagation and figures of merit for quantification by solving matrix equations. Analytical Chemistry 58(6):1167–72. doi: 10.1021/ac00297a042.
   Web of Science ®Google Scholar
• Máquina, A. D. V., L. M. Souza, L. C. Gontijo, D. Q. Santos, and W. B. Neto. 2017. Characterization of biodiesel by infrared spectroscopy with partial least squares discriminant analysis. Analytical Letters 50(13):2117–28. doi: 10.1080/00032719.2016.1267186.
   Web of Science ®Google Scholar
• Muller, A. L. H., R. S. Picoloto, R. C. L. Guimaraes, R. A. Guarnieri, B. M. S. Ferreira, J. C. M. Dias, M. F. P. Santos, E. M. M. Flores, and E. I. Muller. 2013. Determination of basic nitrogen in residues of crude oil distillation using ATR-FTIR and chemometric methods. Analytical Letters 46(18):2879–89. doi: 10.1080/00032719.2013.828299.
   Web of Science ®Google Scholar
• Nørgaard, L., A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, and S. B. Engelsen. 2000. Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54(3):413–9. doi: 10.1366/0003702001949500.
   Web of Science ®Google Scholar
• Olivieri, A. C., N. K. M. Faber, J. Ferré, R. Boqué, J. H. Kalivas, and H. Mark. 2006. Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report). Pure and Applied Chemistry 78(3):633–61. doi: 10.1351/pac200678030633.
   Web of Science ®Google Scholar
• Parisotto, G., M. F. Ferrão, A. L. H. MüLler, E. I. MüLler, M. F. P. Santos, R. C. L. Guimarães, J. C. M. Dias, and E. ́M. M. Flores, 2010. Total acid number determination in residues of crude oil distillation using ATR-FTIR and variable selection by chemometric methods. Energy & Fuels 24(10):5474–8. doi: 10.1021/ef1002974.
   Web of Science ®Google Scholar
• Pasquini, C., and A. F. Bueno. 2007. Characterization of petroleum using near-infrared spectroscopy: Quantitative modeling for the true boiling point curve and specific gravity. Fuel 86(12–13):1927–34. doi: 10.1016/j.fuel.2006.12.026.
   Web of Science ®Google Scholar
• Pedroza, R. H. P., J. T. N. Nicácio, B. S. Santos, and K. M. G. Lima. 2013. Determining the kinematic viscosity of lubricant oils for gear motors by using the near infrared spectroscopy (NIRS) and the wavelength selection. Analytical Letters 46:1145–54. doi: 10.1080/00032719.2012.751542.
   Web of Science ®Google Scholar
• Riazi, M. R. 2005. Characterization and properties of petroleum fractions. 1st ed. West Conshohocken, PA: ASTM Stock Number: MNL50. doi: 10.1520/MNL50-EB.
   Google Scholar
• Rocha, J. T. C., L. M. S. L. Oliveira, J. C. M. Dias, U. B. Pinto, M. L. S. P. Marques, B. P. Oliveira, P. R. Filgueiras, E. V. R. Castro, and M. A. L. Oliveira. 2016. Sulfur determination in Brazilian petroleum fractions by midinfrared and near-infrared spectroscopy and partial least squares associated with variable selection methods. Energy & Fuels 30(1):698–705. doi: 10.1021/acs.energyfuels.5b02463.
   Web of Science ®Google Scholar
• Rodrigues, R. R. T., J. T. C. Rocha, L. M. S. L. Oliveira, J. C. M. Dias, E. I. Müller, E. V. R. Castro, and P. R. Filgueiras. 2017. Evaluation of calibration transfer methods using the ATR-FTIR technique to predict density of crude oil. Chemometrics and Intelligent Laboratory Systems 166:7–13. doi: 10.1016/j.chemolab.2017.04.007.
   Web of Science ®Google Scholar
• Ruschel, C. F. C., M. C. A. Marcelo, F. P. Santos, and M. F. Ferrão. 2017. Characterization of gasoline by 1H nuclear magnetic resonance and chemometrics. Analytical Letters 50(11):1767–77. doi: 10.1080/00032719.2016.1249877.
   Web of Science ®Google Scholar
• Silverstein, R. M., F. X. Webster, and D. J. Kiemle. 2005. Spectrometric identification of organic compounds. 7th ed. New York, NY: John Wiley & Sons.
   Google Scholar
• Snee, R. D. 1977. Validation of regression models: Methods and examples. Technometrics 19(4):415–28. doi: 10.1080/00401706.1977.10489581.
   Web of Science ®Google Scholar
• Soares, I. P., T. F. Rezende, R. C. Silva, E. V. R. Castro, and I. C. P. Fortes. 2008. Multivariate calibration by variable selection for blends of raw soybean oil/biodiesel from different sources using Fourier transform infrared spectroscopy (FTIR) spectra data. Energy & Fuels 22(3):2079–83. doi: 10.1021/ef700531n.
   Web of Science ®Google Scholar
• Speight, J. G. 2002. Handbook of petroleum product analysis. 3rd ed. New York, NY: John Wiley & Sons.
   Google Scholar
• Taylor, J. R. 1997. An introduction to error analysis: The study of uncertainties in physical measurements. 2nd ed. Sausalito, CA: University Science Books.
   Google Scholar
• Trygg, J., and S. Wold. 2002. Orthogonal projections to latent structures (O‐PLS). Journal of Chemometrics 16(3):119–28. doi: 10.1002/cem.695.
   Web of Science ®Google Scholar
• Valderrama, P., J. W. B. Braga, and R. J. Poppi. 2007. Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 55(21):8331–8. doi: 10.1021/jf071538s.
   PubMed Web of Science ®Google Scholar
• Wang, Y., F. Jiang, B. B. Gupta, S. Rho, Q. Liu, H. Hou, D. Jing, and W. Shen. 2017. Variable selection and optimization in rapid detection of soybean straw biomass based on CARS. IEEE Access 6:5290–9. doi: 10.1109/ACCESS.2017.2763596.
   Web of Science ®Google Scholar
• Wilt, B. K., W. T. Welch, and J. G. Rankin. 1998. Determination of asphaltenes in petroleum crude oils by Fourier transform infrared spectroscopy. Energy & Fuels 12(5):1008–12. doi: 10.1021/ef980078p.
   Web of Science ®Google Scholar
• Yan, H., J. Zhang, J. Gao, Y. Huang, Y. Xiong, and S. Min. 2018. Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data. Scientific Reports 8:14729. doi: 10.1038/s41598-018-33022-9.
   PubMed Web of Science ®Google Scholar